"Research Fellow in Digital Chemistry and Engineering"

University of Leeds - Faculty of Engineering & Physical Sciences - School of Chemistry

Are you an experienced and ambitious researcher looking for your next challenge? Do you want to further your career in one of the UK’s leading research-intensive Universities? Are you eager to apply your machine learning expertise to cutting-edge automated optimisation challenges?

We are seeking a Research Fellow in Digital Chemistry and Engineering to combine self-optimising flow reactor technology with high-throughput experimentation (HTE) and apply it to pharmaceutically relevant case studies.

The overarching goal of the project is to advance the sustainability of pharmaceutical manufacturing through innovative automation, analytical science, and data-driven optimisation. You will upgrade a self-optimising flow reactor modular platform integrated with high-throughput experimentation (HTE) and online process analytical technology (PAT), embedding sustainability metrics into the optimisation loop, and apply transfer learning to accelerate the transition from batch to continuous flow processes. You will interact with project partners in the pharmaceutical sector to demonstrate the technology on high impact case studies.

This project is part of a £10M+ project funded by innovate UK and will involve extensive collaboration with industry partners including AstraZeneca, Labman and Britest.

iuk-business-connect.org.uk/news/funding-to-build-a-sustainable-future-for-medicines-manufacturing

The position will be based at the Institute of Process Research and Development, in the School of Chemistry, University of Leeds.

We are open to discussing flexible working arrangements.

To explore the post further or for any queries you may have, please contact:

Prof. Richard Bourne, Professor in Digital Chemical Manufacturing
Email: R.A.Bourne@leeds.ac.uk

OR

Dr Gilian Thomas, Lecturer in Sustainable and Digital Chemistry
Email: G.T.Thomas@leeds.ac.uk